Many recent studies have focused on leveraging rich information types to increase useful information for improving fault localization effectiveness. However, they rarely investigate the impact of information richness on fault localization to give guidance on how to enrich information for improving localization effectiveness. This paper presents the first systematic study to fill this void. Our study chooses four representative information types and investigates the relationship between their richness and the localization effectiveness. The results show that information richness related to frequency execution count involves a high risk of degrading the localization effectiveness, and backward slice is effective in improving localization effectiveness.

In the process of production design, engineers usually find it is difficult to seek and reuse others' empirical knowledge which is in the forms of lesson-learned documents. This study proposed a novel approach, which uses a semantic-based topic knowledge map system (STKMS) to support timely and precisely lesson-learned documents finding and reusing. The architecture of STKMS is designed, which has five major functional modules: lesson-learned documents pre-processing, topic extraction, topic relation computation, topic weights computation, and topic knowledge map generation modules. Then STKMS implementation is briefly introduced. We have conducted two sets of experiments to evaluate quality of knowledge map and the performance of utilizing STKMS in outfitting design of a ship-building company. The first experiment shows that knowledge maps generated by STKMS are accepted by domain experts from the evaluation since precision and recall are high. The second experiment shows that STKMS-based group outperforms browse-based group in both learning score and satisfaction level, which are two measurements of performance of utilizing STKMS. The promising results confirm the feasibility of STKMS in helping engineers to find needed lesson-learned documents and reuse related knowledge easily and precisely.

We have compared the various raindrop-size distributions (DSD) with the recent experimental data collected by the distrometer. It is shown that the Weibull distribution is the best fit to the experimental data for drizzle, widespread and thunderstorm rain cases. By using this Weibull DSD, we obtained a new expression of the radar reflectivity factor (Z) and the rainfall rate (R) relation, that is Z=285R1.48, which gives few errors comparing to some measurements in TRMM frequency of 14GHZ.

This paper is mainly concerned with a knowledge supply model in the environment of knowledge grid to realize the knowledge sharing globally. By integrating members, roles, and tasks in a workflow, three sorts of knowledge demands are gained. Based on knowledge demand information, a knowledge supply model is proposed for the purpose of delivering the right knowledge to the right persons. Knowledge grid, acting as a platform for implementing the knowledge supply, is also discussed mainly from the view of knowledge space. A prototype system of knowledge supply has been implemented and applied in product development.

In this letter we design a new family of space-time block codes (STBC) for multi-input multi-output (MIMO) systems. The complex orthogonal STBC achieves full diversity and full transmission rate with fast maximum-likelihood decoding when only two transmit antennas are employed. By combining the Alamouti STBC and the multidimensional signal constellation rotation based on the cyclotomic number field, we construct cyclotomic orthogonal space-time block codes (COSTBCs) which can achieve full diversity and full rate for multiple transmit antennas. Theoretical analysis and simulation results demonstrate excellent performance of the proposed codes, while the decoding complexity is further reduced.

In this letter, we propose a lattice reduction (LR) aided joint precoding design for MIMO-relay broadcast communication with the average bit error rate (BER) criterion. We jointly design the signal process flow at both the base station (BS), and the relay station (RS), using the reduced basis of two-stage channel matrices. We further modify the basic precoding design with a novel shift method and a modulo method to improve the power efficiency at the BS and the RS respectively. In addition, the MMSE-SIC algorithm is employed to improve the performance of precoding. Simulations show that, the proposed schemes achieve higher diversity order than the traditional precoding without LR, and the modified schemes significantly outperform the basic design, proving the effectiveness of the proposed methods.

We extend the Nonparametric Discriminant Analysis (NDA) algorithm to a semi-supervised dimensionality reduction technique, called Semi-supervised Nonparametric Discriminant Analysis (SNDA). SNDA preserves the inherent advantages of NDA, that is, relaxing the Gaussian assumption required for the traditional LDA-based methods. SNDA takes advantage of both the discriminating power provided by the NDA method and the locality-preserving power provided by the manifold learning. Specifically, the labeled data points are used to maximize the separability between different classes and both the labeled and unlabeled data points are used to build a graph incorporating neighborhood information of the data set. Experiments on synthetic as well as real datasets demonstrate the effectiveness of the proposed approach.